Climate variability and extreme weather events threaten many populations throughout the world [1
]. The evidence indicates that, in many of these regions, variability and extreme events are increasing [2
]. In recent years, droughts have received a special attention in Brazil, because they have been experienced with higher frequency, spatial extent, severity, and duration [4
]. Some studies have also revealed that prolonged droughts and increased evaporation can lead to reduced water supply, crop failure, and diminished power generation, causing dramatic societal effects such as water rationing and massive electricity blackouts [4
Although most regions of Brazil have suffered extreme droughts, their impacts are significantly more complex in the semiarid region of Northeast Brazil (NEB) due to its high variability of precipitation in both time and space [8
]. It is also the world’s most densely populous dry land region [11
]. Historically, NEB has been hit by many droughts. However, the 2012/2015 drought was one of the most severe in the recent decades with more than 10 million people affected in the semi-arid region [14
]. This extreme condition in the northeast was linked to the deficit of rainfall and drying conditions that contributed to reduced soil water availability [15
The majority of smallholder farmers living in NEB rely on subsistence agriculture, therefore droughts often trigger water shortage, leading to crop and economic losses [11
]. Different types of drought are recognized in the world, including meteorological, agricultural, and hydrological drought, depending on the variable used to characterize this natural hazard and its spatial and temporal scale [19
]. Unlike other types of droughts, the agricultural drought has a direct impact on rainfed-based agricultural production [21
]. Usually, an agricultural drought is considered to begin when the soil moisture availability to plants drops to such a level that it unfavorably affects the crop yield and therefore agricultural production [22
As already mentioned, droughts are the main cause of limited productivity of rainfed agriculture in NEB [24
]. Hence, assessment of agricultural drought has a primary importance for rainfed agriculture planning and management. Several drought indices based on combination measures of precipitation, temperature and soil moisture, have been derived in recent decades to assess the effects of agricultural droughts and besides measure their intensity, duration, severity and spatial extent [26
]. Among these, the Crop Moisture Index (CMI; Palmer, 1968), Atmospheric Water Deficit (AWD; [28
]), Soil Moisture Index (SMI; [29
]), Agricultural Reference Index for Drought (ARID; [30
]), and Soil Water Deficit Index (SWDI; [22
]) are the most widely used for monitoring drought conditions on extensive crops (e.g., cereals).
The amount of available soil moisture in the root zone is a more critical factor for crop growth than the amount of precipitation deficit or excess. In fact, the soil moisture deficit in the root zone during various stages of the crop growth cycle has a profound impact on the crop yield [31
]; therefore, the accurate knowledge of soil moisture is a key aspect for the characterization of agricultural droughts. Ground-based soil moisture measurements are very accurate, but they have an application limited because of their point-based nature, their reduced spatial extent, and the high variability of soils [27
]. Nevertheless, that limitation has been gradually overcome due to progress in the development of satellite technology and retrieval algorithms for quantifying soil moisture from active and passive microwave satellite platforms [32
]. Currently, these estimates are used to detect and monitor regions affected by droughts and have the advantage of their wide spatial distribution and coverage, as well as the temporal availability of data [35
The remote sensing approach for drought monitoring has been enriched with the launch of new missions dedicated to global surface soil moisture (SSM) monitoring. For instance, the Soil Moisture and Ocean Salinity (SMOS) satellite launched in early November 2009 by European Space Agency (ESA), and the Soil Moisture Active Passive (SMAP) satellite launched in January 2015 by National Aeronautics and Space Administration (NASA). SMOS is an L-band passive microwave satellite that measures brightness temperatures with Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) [35
SMOS is opening new perspectives for monitoring the effects of agricultural droughts over large agricultural regions [35
]. In this sense, some recent studies have proposed new indices based on soil moisture estimates derived from SMOS to assess agricultural drought. Scaini et al. [37
] demonstrated the feasibility of SMOS-derived soil moisture anomalies for determining drought conditions in a central semiarid sector of the Duero basin in Spain. In this same region, Martínez-Fernández et al. [36
] compared series of the SWDI calculated with SMOS L2 data with ones obtained from in situ soil moisture data, and their results showed that SMOS-derived SWDI reproduces well the soil water balance dynamic. More recently, Sánchez et al. [42
] introduced a new index, so-called the Soil Moisture Agricultural Drought Index (SMADI), which is a synergistic fusion of the SMOS L2 soil moisture with the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and several water/vegetation indices for agricultural drought monitoring. These authors demonstrated that SMADI could provide early warning of incipient drought impacts in rainfed farming systems.
Despite the great potential of SMOS for agricultural drought monitoring, very few works have been published using soil moisture derived from SMOS data in worldwide; especially in Brazil. For instance, Rossato and Angelis [43
] used data of brightness temperature sensor MIRAS aboard the SMOS satellite to assess the pattern of soil moisture in densely vegetated areas in some Brazilian locations with soil moisture data measured in situ. They have reported that the SMOS-derived data infer accurate values of soil moisture over those areas, which demonstrated the feasibility of SMOS to support in planning for planting and/or irrigation of crops. Over NEB, Ferreira et al. [44
] found that the soil moisture changes estimated by a Model of Soil Moisture for Agricultural Activities (MUSAG) and those derived from SMOS show a significant correlation for 184 selected sites within the Ceará state. Their results indicated that SMOS data might represent the soil moisture change in the Brazilian Caatinga satisfactorily, allowing assessment of the seasonality of the water balance in this region.
In this work, the focus is specifically on the entire NEB, which has been hit by an unprecedented drought since 2012. Severe droughts have caused serious impacts on water supply and agriculture of the NEB, especially during the rainy season [8
]. The main goal of this work is to compare the series of the SWDI calculated with SMOS L2 data with those of the Atmospheric Water Deficit derived from in situ observations, which is used as a reference index. We also analyzed its feasibility for large-scale agricultural drought monitoring. It is important highlight that in none of previous study has been examined the SMOS-derived SWDI as a proxy of upper soil moisture in the NEB.
In line with the results from some previous studies [26
], it was found that the atmospheric conditions over NEB influences the upper soil water balance dynamic. This characteristic is reflected by a moderate coupling strength between the AWD and SWDIS from June 2010 to December 2013 in the entire NEB (Figure 2
and Figure 3
). There, it was also possible to identify different levels of bias between both signals. As expected, the AWD cannot be directly compared to the SWDIS, as the variation range of the first was larger (−59 ≤ AWD ≤ 406) than that of the second (−16 ≤ SWDIS ≤ 52) (Figure 2
a). Consequently, one could expect that a negative value of the AWD will be reflected by a negative SWDIS value, but with lower magnitude.
Results revealed that during the 2012–2015 drought the central Sertão region was exposed to drought conditions more severe than other regions of the NEB, which is consistent with find by [13
]. It is interesting to indicate that the Sertão region showed high radio frequency interference (RFI) during the analyzed period, leading to SWDIS time series with high proportion of gaps. On the other side, according to Hengl et al. [72
], various zones of the Sertão region have argillic horizons very near the surface, which could have favored the soil water retention in surface soil layer during the occurrence of a drought episode. Both physical factors could be related to the poor performance of the SWDIS in some areas of the Sertão region (Figure 3
In terms of detection of drought weeks, the SWIDS showed a moderately high skill (Figure 4
). One implication of this result is that there is great potential in using SWDIS to monitor weekly dry spells. The same conclusion was arrived at by Martínez-Fernández et al. [36
], who found that the drought periods are relatively well captured by the SWDIS at the central part of Spain, which has a similar climatic regime to the NEB (Figure 5
Other relevant aspects were observed at the local scale. For instance, according to Hengl et al. [72
], Carnaubais and Campo Maior, where the SWDIS-AWD relationship was stronger (Figure 6
a,b), show Ultisols with a water table near the surface for much of the year (Table 1
). Furthermore, Correntina and Fazendas do Piauí, where the SWDIS-AWD relationship was weaker (Figure 6
c,d), present Oxisols and Ultisols, respectively, as their dominant soils (Table 1
). It is well known that the amount of clay and sand has an important influence on the soil water dynamic [22
]. In fact, soil water retention is strongly correlated to the clay content [78
]. In this context, one could suppose that clay soils may show positive values of SWDIS, in spite of the presence of severe drought conditions (i.e., AWD << 0), leading to values of correlation near to zero or negative when the AWD and SWDIS are compared. About this point, results suggest that the AWD-SWDIS relationship was not influenced significantly by the soil texture. Evidence of this is that this physical attribute did not show a statistically significant correlation coefficient with respect to the AWD-SWDIS correlation when were taken into account all benchmark sites (Figure 7
b–d). Nevertheless, caution must be taken about this hypothesis due to that the amount of benchmark sites represent less than 2% of the size of the sample (2050 RDG cells).
One of the advantages of SMOS is that it can observe at a lower frequency (1.4 GHz) than previous instruments (e.g., NASA/JAXA Advanced Scanning Microwave Radiometer (AMSR-E)) [79
]. This frequency is less affected by the vegetation cover [64
]. Moreover, the soil moisture retrievals on forest have been improved with the new level 2 V620 algorithm [80
], which was the version used in this study. These features of SMOS help to explain why the influence of vegetal cover/use on the coupling strength between the AWD and SWDIS in the benchmark sites was little (see Table 1
). However, despite this local response, results suggest that the SWDIS does not capture enough well the temporal dynamics of the agricultural drought in semiarid biomes such as in the Caatinga (Figure 3
and Figure 4
). Previous studies have already shown that SMOS has a relatively low ability in arid and semiarid biomes [65
], therefore, this result has been consistent.
A critical issue of soil moisture retrieval from SMOS is that their estimations are limited to the first centimeters of the surface soil layer (i.e., 0–5 cm) and depend on soil water content [36
]. This water is stored not only in the surface layer but also in the root-zone layer [26
]. Although some studies have demonstrated the presence of a strong correlation between the content of moisture in surface and root-zone [67
], the coupling strength among soil layers decreases as depth increases, and moreover depend on the prevailing hydrometeorological conditions [36
]. As already mentioned, the climatic conditions of the NEB during the period of analysis (i.e., June 2010–December 2013) were characterized by persistent drought conditions, which intensified near to end of 2012 and extended until 2015 [13
]. It is convenient to mention that previous studies have indicated that the SMI–AWD relationship tend to be weaker in the drier years [29
]. During the driest episodes, the atmospheric and soil dynamics are more disconnected, because the water transfer is mainly controlled by soil characteristics [22
]. This physical feature would explain partially the AWD-SWDIS decoupling observed in sites such as São Raimundo Nonato (Figure 6
d), where Marengo et al. [13
] found the level of dryness most severe throughout the 2012–2015 drought. Note that this situation of extreme dryness can be observed in Figure 7
f by the fact that the AWD was negative persistently at São Raimundo Nonato (see point # 4) over the time span of June 2010 to December 2013.
The physical factor that has more influence on the AWD-SWDIS coupling for the entire NEB is terrain elevation (Figure 7
a). In general, the SWDIS shows better performance in open flatland than in areas with complex relief. The same behavior has previously been found for the retrieval of soil moisture from SMOS with respect to relief, and it has been attributed mainly to surface roughness [87
Summarizing, the above results suggest that the topographical factors and the level of atmospheric dryness have played a key role in the overall performance of the SWDIS as a proxy for agriculture drought at local and regional scales. Overall, when the dryness level is persistently negative over time (i.e., AWD << 0) in mountain regions (e.g., Correntina), the SWDIS tends to show poor performance. On the other hand, the vegetal cover/use and soil texture are physical attributes that have a marked influence local, whereas that the RFI and biomes are dominant on the global performance of the SWDIS.
Although the results presented in this study are limited in terms of the time span of SWDIS (approximately four years), this index seems to be reliable when it is used to identify the beginning, end, and duration of a drought episode, because these attributes are inferred from the dry spells (i.e., drought weeks). However, the assessment of the SWDIS as a proxy of the superficial soil moisture deficit in the NEB should be further investigated by considering other agricultural drought indices obtained from in situ data, such as the Crop Moisture Index [89
]. In any case, it should be pointed out that these preliminary results are very promising for the NEB, since in the future one could obtain the SWDI from exclusively SMOS data. The SWDIS could be also a suitable tool to monitor the drought dynamics related to soil water storage in open and low-elevation flatlands of NEB. In this context, its operative implementation may facilitate the preparation of drought plans by national government decision makers.
NEB suffers from regular droughts, particularly inside the semi-arid Sertão region. Future climate projections suggest temperature increases and rainfall reductions in this region, which would affect the rainfed crop yields such as corn, sugarcane, and cotton. In this context, drought monitoring and early warning systems are needed to improve the level of preparedness for agricultural drought. Soil moisture is among the more reliable physical parameters for tracking the effects of droughts on soils. Currently, soil moisture may be retrieved from space using a new generation of satellites such as SMOS, which provide a unique opportunity to incorporate remote sensing tools into agricultural drought monitoring.
In this work, the agricultural drought index (SWDI), based on soil moisture content derived from the SMOS satellite (SWDIS) for a weekly scale, has been assessed for the first time as a proxy of the superficial soil moisture deficit in NEB. Several calculation approaches have been applied to measure its overall performance at large-scale and local-scale. The SWDIS data have been compared with the Atmospheric Water Deficit (AWD) calculated from in situ ETo and rainfall data, and an acceptable correlation was obtained. Results also revealed that the SWDIS reproduces soil water balance dynamic relatively well at low-elevation flatlands of the NEB; thus, it could be a feasible tool for agricultural drought monitoring. With regard to the performance of the SWDIS, the vegetal cover/use and soil texture have a marked local influence, but the RFI, biomes, and elevation seem to be more influential at global scale. Nevertheless, it is obvious that, although good results have been obtained, it is necessary to assess the SWDIS with the SWDI derived from longer soil moisture data or other agricultural drought indices.